Machine Learning-Based Prediction of First Trimester Down Syndrome Risk in East Asian Populations

Abstract

Purpose: Down syndrome is the most common chromosomal abnormality in newborns, often leading to developmental delays and congenital structural anomalies. This study employed multiple machine learning models to perform risk prediction and result exploration for first-trimester Down syndrome in East Asian populations, aiming to identify an optimal risk prediction model that will enhance future predictions of Down syndrome risk and improve the efficiency of the screening process. Patients and Methods: This study collected data from the Down syndrome screening database at Taipei Chang Gung Memorial Hospital from May 1, 2018, to February 29, 2024. The dataset included 3,812 cases available for analysis, comprising 165 high-risk cases and 3,647 low-risk cases. Fourteen features (including maternal age, nuchal translucency thickness, serum markers, etc.) were input into the twelve machine learning models, along with seven data-balancing algorithms, to explore the risk prediction outcomes. The performance of these models was thoroughly evaluated using AUC (Area Under the Curve), accuracy, precision, recall, and F1 scores. Results: Among the twelve machine learning models, the highest recall of 0.84 for high-risk cases was achieved by LightGBM combined with the RUS (Random Undersampling) data balancing algorithm. The highest AUC of 0.939 was attained by the ANN and LSTM models when combined with the ROS (Random Oversampling) data balancing algorithm. Conclusion: The proposed ANN machine learning model, based on deep neural networks and combined with the ROS data balancing method, achieved an impressive AUC of 0.939 for classifying first-trimester Down syndrome risk in the East Asian population. Notably, this model also achieved an outstanding classification accuracy of 0.97. These results demonstrate the potential of the proposed ANN machine learning model for the accurate prediction of first-trimester Down syndrome risk.